sklearn.datasets.load_digits

sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False) [source]

Load and return the digits dataset (classification).

Each datapoint is a 8x8 image of a digit.

Classes	10
Samples per class	~180
Samples total	1797
Dimensionality	64
Features	integers 0-16

Read more in the User Guide.

Parameters

n_classint, default=10

The number of classes to return. Between 0 and 10.

return_X_ybool, default=False

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

New in version 0.18.

as_framebool, default=False

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

New in version 0.23.

Returns

dataBunch

Dictionary-like object, with the following attributes.

data{ndarray, dataframe} of shape (1797, 64)

The flattened data matrix. If as_frame=True, data will be a pandas DataFrame.

target: {ndarray, Series} of shape (1797,)

The classification target. If as_frame=True, target will be a pandas Series.

feature_names: list

The names of the dataset columns.

target_names: list

The names of target classes.

New in version 0.20.

frame: DataFrame of shape (1797, 65)

Only present when as_frame=True. DataFrame with data and target.

New in version 0.23.

images: {ndarray} of shape (1797, 8, 8)

The raw image data.

DESCR: str

The full description of the dataset.

(data, target)tuple if return_X_y is True

New in version 0.18.

This is a copy of the test set of the UCI ML hand-written digits datasets

https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Examples

To load the data and visualize the images:

>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import matplotlib.pyplot as plt 
>>> plt.gray() 
>>> plt.matshow(digits.images[0]) 
>>> plt.show() 

Examples using sklearn.datasets.load_digits